One or more embodiments of the invention relate generally to the field of three-dimensional (3D) image rendering. More particularly, the invention relates to a method and apparatus for point cloud assembly.
Driven by the fields of computer vision, as well as range data processing, the real-time generation of three-dimensional (3D) images from a three-dimensional object within a computer system may one day become a reality. Generally, the process requires reverse engineering of accurate 3D models of real objects from, for example, 3D surface measurements or photographs. For example, given two sets of data and an initial estimate of the relative position, the iterative closest point algorithm (ICP) is used to register (align) the data sets by improving the position and orientation estimates.
Accordingly, the goal of the registration is to transform sets of surface measurements into a common coordinate system. However, capturing a complete object surface generally requires multiple range images from different viewpoints. Once these images are acquired, the various images must be combined (registered) utilizing algorithms, such as the ICP algorithm referred to above. Unfortunately, algorithms such as the ICP algorithm are dependent upon the initial position and orientation estimates. Moreover, the ICP approach, along with other data registration techniques perform in an iterative fashion.
Specifically, at each ICP iteration, correspondences are determined between the two data sets and a transformation is computed, which minimizes the mean square error (MSE) of the correspondences. Consequently, the iterations continue until either the MSE falls below some threshold value, the MSE reaches a local minima, or the maximum number of iterations is exceeded. Unfortunately, due to its fairly large computational expense, ICP is typically considered to be a batch, or at best, user-guided process, where users initiate and assist the process and then allow the process to execute, often overnight.
Other approaches for registering sets of data relative to a common coordinate system are provided. However, the various registration solutions also operate based on position and orientation estimates. As such, these additional registration algorithms can also have conversion problems, and based on the geometry of the images, may have problems when attempting to recreate a three-dimensional representation of an object. Moreover, image capture utilizing these techniques is generally effected utilizing a laser. However, due to the laser-assisted image acquisition, context information is generally not available. As such, high-speed three-dimensional data scanners are currently unavailable, which utilize texture information in addition to geometry to perform registration of data sets relative to a common coordinate system. Therefore, there remains a need to overcome one or more of the limitations in the above-described, existing art.